Scaling Laws papers, 2023–2026

Key findings

• Scaling Laws went from 60 papers in 2023 to 248 in 2026, roughly 4.1x in three years.
• NeurIPS publishes the most Scaling Laws work: 221 papers from 2023 to 2026, 37% of the four-venue total.
• The fastest-growing overlap is with Time Series: papers tagged with both rose from 1 in 2023 to 24 in 2026.
• Among the 5 topics under Optimization and Scaling, it ranks #5 by 2026 paper count; the fastest grower in the group, Low-Rank Adaptation, is up 3.0x versus 4.1x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Unified Merge Calculus for Learning-Rate Scaling on Neural Computation Graphs
• ICML 2026A Capacity-Based Rationale for Multi-Head Attention
• ICLR 2026$\mathbf{Li_2}$: A Framework on Dynamics of Feature Emergence and Delayed Generalization
• CVPR 2026A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling
• NeurIPS 2025Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives
• ICML 2025A Simple Model of Inference Scaling Laws
• ICLR 2025(Mis)Fitting Scaling Laws: A Survey of Scaling Law Fitting Techniques in Deep Learning
• CVPR 2025D^3: Scaling Up Deepfake Detection by Learning from Discrepancy
• NeurIPS 2024An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem
• ICML 2024A Dynamical Model of Neural Scaling Laws
• ICLR 2024Principled Architecture-aware Scaling of Hyperparameters
• CVPR 2024From Activation to Initialization: Scaling Insights for Optimizing Neural Fields
• NeurIPS 2023Abide by the law and follow the flow: conservation laws for gradient flows
• ICML 2023Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
• ICLR 2023Distributionally Robust Post-hoc Classifiers under Prior Shifts
• CVPR 2023LargeKernel3D: Scaling Up Kernels in 3D Sparse CNNs
• NeurIPS 2026ADIS-Law: Unified Scaling Laws for Annealing-Phase Domain Injection in Large Language Models
• ICML 2026Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
• ICLR 2026A universal compression theory: Lottery ticket hypothesis and superpolynomial scaling laws
• CVPR 2026MatAnyone 2 Scaling Video Matting via a Learned Quality Evaluator

All Scaling Laws papers

248 papers, page 4 of 5

151. ICML 2026Selling Data as a Digital Good with Scaling Valuations
152. ICML 2026Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws
153. ICML 2026Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
154. ICML 2026Statistically Calibrated Scaling for Token Merging in Transformers
155. ICML 2026Temperature Scaling in Discrete Sequence (Language) Models
156. ICML 2026Token Sample Complexity of Attention
157. ICML 2026TQL: Scaling Q-Functions with Transformers by Preventing Attention Collapse
158. ICML 2026Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
159. ICML 2026Understanding MARS: When Scaling Momentum Provably Helps
160. ICML 2026Universal One-third Time Scaling in Learning Peaked Distributions
161. ICML 2026Utility Boundary of Dataset Distillation: Scaling and Coverage Laws
162. ICML 2026ViTok-v2: Scaling Native Resolution Autoencoders to 5 Billion Parameters
163. ICML 2026WF-Bench: A Benchmark for Neural-Network WaveFunction Expressivity and Scaling Laws
164. ICML 2026When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
165. ICML 2026When Does Adaptation Win? Scaling Laws for Meta-Learning in Quantum Control
166. ICML 2026Who Said Neural Networks Aren't Linear?
167. NeurIPS 2026A Revisit of Hamiltonian Monte Carlo Efficiency on Bayesian Neural Networks
168. NeurIPS 2026A Unified Merge Calculus for Learning-Rate Scaling on Neural Computation Graphs
169. NeurIPS 2026ADIS-Law: Unified Scaling Laws for Annealing-Phase Domain Injection in Large Language Models
170. NeurIPS 2026ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling
171. NeurIPS 2026Asymmetric Scaling Laws from Sparse Features
172. NeurIPS 2026Bentkus-type asymptotic e-values
173. NeurIPS 2026BIRD-RL: Scaling Agentic Reinforcement Learning over Stateful Data-Centric Environments
174. NeurIPS 2026CLIFT: Conformal Self-Verification for Web Agent Training and Test-Time Scaling
175. NeurIPS 2026Compute-optimal data scaling for neural surrogates via multi-fidelity training
176. NeurIPS 2026Conservation Laws for Diffusion Models
177. NeurIPS 2026Consilience for Verifier-Free Test-Time Scaling
178. NeurIPS 2026Data-Constrained Language Model Pretraining: Improved Regularization and Scaling Laws
179. NeurIPS 2026Decomposing and Reshaping Scaling Laws through Token Learning Times
180. NeurIPS 2026Deriving Hyperparameter Scaling Laws via Modern Optimization Theory
181. NeurIPS 2026Distributionally Robust Mixture-of-Experts Training
182. NeurIPS 2026DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers
183. NeurIPS 2026Early Signals, Strong Decisions: Prefix-Guided Sampling for Parallel Test-Time Scaling
184. NeurIPS 2026Efficient Decoder Scaling Strategy for Constructive Neural Routing Solvers
185. NeurIPS 2026Efficient Scaling of LLM Training with Flexible Context Parallelism
186. NeurIPS 2026Exact Recovery of Lipschitz Orthogonal Coordinate Transformations via Constrained Normalizing Flows
187. NeurIPS 2026ExComm: Exploration-Stage Communication for Error-Resilient Agentic Test-Time Scaling
188. NeurIPS 2026Holistic Scaling Laws for Optimal Mixture-of-Experts Architecture Optimization
189. NeurIPS 2026How Can SignSGD Outperform SGD? A Functional Scaling Law Perspective
190. NeurIPS 2026How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models
191. NeurIPS 2026Hyperbolic Language Models: From Zipf to Compute-Optimal Scaling
192. NeurIPS 2026It Just Takes Two: Scaling Amortized Inference to Large Sets
193. NeurIPS 2026Language Denoising Objectives Extend the Value of Limited Data
194. NeurIPS 2026Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory
195. NeurIPS 2026Meta-memorization and memorization scaling laws in transformers
196. NeurIPS 2026MobileMoE: Scaling On-Device Mixture of Experts
197. NeurIPS 2026Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer
198. NeurIPS 2026Neural Neural Scaling Laws
199. NeurIPS 2026Neuron Populations Exhibit Divergent Selectivity with Scale
200. NeurIPS 2026Norm Anchors Make Model Edits Last

FAQ

How many Scaling Laws papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

605 in total: 60 in 2023, 96 in 2024, 201 in 2025, 248 in 2026.

How many Scaling Laws papers are at NeurIPS 2026?

NeurIPS 2026 has 82 Scaling Laws papers, versus 80 the year before.

How many Scaling Laws papers are at ICLR 2026?

ICLR 2026 has 73 Scaling Laws papers, versus 48 the year before.

How many Scaling Laws papers are at ICML 2026?

ICML 2026 has 78 Scaling Laws papers, versus 56 the year before.

How many Scaling Laws papers are at CVPR 2026?

CVPR 2026 has 15 Scaling Laws papers, versus 17 the year before.

Which conference publishes the most Scaling Laws papers?

NeurIPS, with 221 papers from 2023 to 2026.